本文整理汇总了Python中chainer.functions.clipped_relu方法的典型用法代码示例。如果您正苦于以下问题：Python functions.clipped_relu方法的具体用法？Python functions.clipped_relu怎么用？Python functions.clipped_relu使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类chainer.functions的用法示例。
在下文中一共展示了functions.clipped_relu方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x, t=None):
self.clear()
#x = Variable(x_data) # x_data.astype(np.float32)
h = F.leaky_relu(self.conv1(x), slope=0.1)
h = F.leaky_relu(self.conv2(h), slope=0.1)
h = F.leaky_relu(self.conv3(h), slope=0.1)
h = F.leaky_relu(self.conv4(h), slope=0.1)
h = F.leaky_relu(self.conv5(h), slope=0.1)
h = F.leaky_relu(self.conv6(h), slope=0.1)
h = F.clipped_relu(self.conv7(h), z=1.0)
if self.train:
self.loss = F.mean_squared_error(h, t)
return self.loss
else:
return h 示例2: forward
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def forward(self, inputs, device):
x, = inputs
y = functions.clipped_relu(x, self.z)
return y, 示例3: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x, t=None):
self.clear()
h = F.leaky_relu(self.conv1(x), slope=0.1)
h = F.leaky_relu(self.conv2(h), slope=0.1)
#h = F.leaky_relu(self.conv3(h), slope=0.1)
#h = F.leaky_relu(self.conv4(h), slope=0.1)
h = F.clipped_relu(self.conv3(h), z=1.0)
if self.train:
self.loss = F.mean_squared_error(h, t)
return self.loss
else:
return h 示例4: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x, t=None):
self.clear()
h1 = F.leaky_relu(self.conv1(x), slope=0.1)
h1 = F.leaky_relu(self.conv2(h1), slope=0.1)
h1 = F.leaky_relu(self.conv3(h1), slope=0.1)
h2 = self.seranet_v1_crbm(x)
# Fusion
h12 = F.concat((h1, h2), axis=1)
lu = F.leaky_relu(self.convlu6(h12), slope=0.1)
lu = F.leaky_relu(self.convlu7(lu), slope=0.1)
lu = F.leaky_relu(self.convlu8(lu), slope=0.1)
ru = F.leaky_relu(self.convru6(h12), slope=0.1)
ru = F.leaky_relu(self.convru7(ru), slope=0.1)
ru = F.leaky_relu(self.convru8(ru), slope=0.1)
ld = F.leaky_relu(self.convld6(h12), slope=0.1)
ld = F.leaky_relu(self.convld7(ld), slope=0.1)
ld = F.leaky_relu(self.convld8(ld), slope=0.1)
rd = F.leaky_relu(self.convrd6(h12), slope=0.1)
rd = F.leaky_relu(self.convrd7(rd), slope=0.1)
rd = F.leaky_relu(self.convrd8(rd), slope=0.1)
# Splice
h = CF.splice(lu, ru, ld, rd)
h = F.leaky_relu(self.conv9(h), slope=0.1)
h = F.leaky_relu(self.conv10(h), slope=0.1)
h = F.leaky_relu(self.conv11(h), slope=0.1)
h = F.clipped_relu(self.conv12(h), z=1.0)
if self.train:
self.loss = F.mean_squared_error(h, t)
return self.loss
else:
return h 示例5: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x, t=None):
self.clear()
h = F.leaky_relu(self.conv1(x), slope=0.1)
h = F.leaky_relu(self.conv2(h), slope=0.1)
h = F.leaky_relu(self.conv3(h), slope=0.1)
h = F.leaky_relu(self.conv4(h), slope=0.1)
h = F.leaky_relu(self.conv5(h), slope=0.1)
h = F.leaky_relu(self.conv6(h), slope=0.1)
h = F.clipped_relu(self.conv7(h), z=1.0)
if self.train:
self.loss = F.mean_squared_error(h, t)
return self.loss
else:
return h 示例6: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x):
for nth in range(self.layers):
if getattr(self, 'P' + str(nth)) is None:
setattr(self, 'P' + str(nth), variable.Variable(
self.xp.zeros(self.sizes[nth], dtype=x.data.dtype),
volatile='auto'))
E = [None] * self.layers
for nth in range(self.layers):
if nth == 0:
E[nth] = F.concat((F.relu(x - getattr(self, 'P' + str(nth))),
F.relu(getattr(self, 'P' + str(nth)) - x)))
else:
A = F.max_pooling_2d(F.relu(getattr(self, 'ConvA' + str(nth))(E[nth - 1])), 2, stride = 2)
E[nth] = F.concat((F.relu(A - getattr(self, 'P' + str(nth))),
F.relu(getattr(self, 'P' + str(nth)) - A)))
R = [None] * self.layers
for nth in reversed(range(self.layers)):
if nth == self.layers - 1:
R[nth] = getattr(self, 'ConvLSTM' + str(nth))((E[nth],))
else:
upR = F.unpooling_2d(R[nth + 1], 2, stride = 2, cover_all=False)
R[nth] = getattr(self, 'ConvLSTM' + str(nth))((E[nth], upR))
if nth == 0:
setattr(self, 'P' + str(nth), F.clipped_relu(getattr(self, 'ConvP' + str(nth))(R[nth]), 1.0))
else:
setattr(self, 'P' + str(nth), F.relu(getattr(self, 'ConvP' + str(nth))(R[nth])))
return self.P0 示例7: __init__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __init__(self,
in_channels,
out_channels,
expansion_size=expand_input_by_factor(6),
expand_pad='SAME',
depthwise_stride=1,
depthwise_ksize=3,
depthwise_pad='SAME',
project_pad='SAME',
initialW=None,
bn_kwargs={}):
super(ExpandedConv2D, self).__init__()
with self.init_scope():
if callable(expansion_size):
self.inner_size = expansion_size(num_inputs=in_channels)
else:
self.inner_size = expansion_size
def relu6(x):
return clipped_relu(x, 6.)
if self.inner_size > in_channels:
self.expand = TFConv2DBNActiv(
in_channels,
self.inner_size,
ksize=1,
pad=expand_pad,
nobias=True,
initialW=initialW,
bn_kwargs=bn_kwargs,
activ=relu6)
depthwise_in_channels = self.inner_size
else:
depthwise_in_channels = in_channels
self.depthwise = TFConv2DBNActiv(
depthwise_in_channels,
self.inner_size,
ksize=depthwise_ksize,
stride=depthwise_stride,
pad=depthwise_pad,
nobias=True,
initialW=initialW,
groups=depthwise_in_channels,
bn_kwargs=bn_kwargs,
activ=relu6)
self.project = TFConv2DBNActiv(
self.inner_size,
out_channels,
ksize=1,
pad=project_pad,
nobias=True,
initialW=initialW,
bn_kwargs=bn_kwargs,
activ=None) 示例8: __call__
# 需要导入模块: from chainer import functions [as 别名]
# 或者: from chainer.functions import clipped_relu [as 别名]
def __call__(self, x, train=False):
"""
calculate output of VoxResNet given input x
Parameters
----------
x : (batch_size, in_channels, xlen, ylen, zlen) ndarray
image to perform semantic segmentation
Returns
-------
proba: (batch_size, n_classes, xlen, ylen, zlen) ndarray
probability of each voxel belonging each class
elif train=True, returns list of logits
"""
with chainer.using_config("train", train):
h = self.conv1a(x)
h = F.relu(self.bnorm1a(h))
h = self.conv1b(h)
c1 = F.clipped_relu(self.c1deconv(h))
c1 = self.c1conv(c1)
h = F.relu(self.bnorm1b(h))
h = self.conv1c(h)
h = self.voxres2(h)
h = self.voxres3(h)
c2 = F.clipped_relu(self.c2deconv(h))
c2 = self.c2conv(c2)
h = F.relu(self.bnorm3(h))
h = self.conv4(h)
h = self.voxres5(h)
h = self.voxres6(h)
c3 = F.clipped_relu(self.c3deconv(h))
c3 = self.c3conv(c3)
h = F.relu(self.bnorm6(h))
h = self.conv7(h)
h = self.voxres8(h)
h = self.voxres9(h)
c4 = F.clipped_relu(self.c4deconv(h))
c4 = self.c4conv(c4)
c = c1 + c2 + c3 + c4
if train:
return [c1, c2, c3, c4, c]
else:
return F.softmax(c)